Window manufacturers typically receive orders that include a variety of different sizes and types of windows and/or patio doors. The different sizes and types of windows and/or patio doors require different sizes and types of insulating glass units (IGs) that are assembled into a frame or sash to form a completed window or patio door at one or more glazing lines. The window manufacturers separate and group the orders for the IGs into regular or planned production runs. The regular or planned production runs are scheduled to be manufactured in a certain sequence on a certain future date, usually within one to three business days ahead.
Variables in the manufacturing process rarely allow the regular production runs to be manufactured in the exact planned sequence. For example, rush orders for important customers and remake orders that occur when IGs break are often prioritized, changing the sequence of the production runs. The operational status of machines used to make the components of the IGs may also cause the sequence of the regular or planned production runs to be altered. Further, demand fluctuations, such as a shortage at one of the glazing lines may cause the sequence of the regular or planned production runs to be altered. As a result, a supervisor of an IG production line must constantly monitor each of the manufacturing variables and modify the sequence of the production runs accordingly.
Current methods employed by IG supervisors for monitoring IG manufacturing variables and modifying production run sequences are slow, inaccurate and confusing. The existing methods typically rely on informal communications, such as word of mouth, handwritten documents and manual data entry. Use of these non-automated forms of communication often confuse operators, tie up machines and delay standard manufacturing procedures. Use of these informal communication methods cause production efficiencies to drop even further while new employees are being trained or new machines are being commissioned.
The glass lites that are needed to construct the IGs are separated and grouped into scheduled production batches or runs. For each production batch, the glass lites are further grouped and arranged to be cut from large stock glass sheets to achieve the highest yield. The process of grouping and arranging glass lites to be cut from stock glass sheets to achieve the highest yield is called glass optimization.
Glass optimization is usually performed by a computer executing a computer program. The output from the glass optimization process is a control program that is sent to a computer-controlled cutting table. The glass optimization software outputs a computer program that optimizes one or more production batches containing patterns of lites arranged on stock glass sheets. The cutting table automatically scores the glass according to each pattern. Each production batch normally contains one or more glass layout patterns that provide a lower yield than desirable.
These Low Yield Patterns or Low Yield Sheets significantly reduce the yield of entire production batches resulting in higher manufacturing costs due to wasted glass. Waste is particularly expensive when manufacturing windows from increasingly popular specialty glasses such as Low-E or self-cleaning materials.
Today, there are several existing methodologies used to increase glass yields. Unfortunately, each method presents one or more problems to manufacturing operations. The methods and their resulting problems are described below.                a) Standard dimensioned lites called filler lites can be introduced to scheduled production batches to fill-in unused space on the stock glass sheets. The glass optimization software determines where filler lites can be inserted when creating the initial programmed patterns. Because fillers are inserted prior to the actual manufacturing process, the number and type of filler lites rarely meet actual production demand. Too few filler lites starve production lines while too many fillers create storage and quality problems.        b) Adding different sizes of large sheets can be stocked to increase yield. This allows the glass optimization software to pick the size of stock sheets that produce the best yield. Although this method enhances yield, it also increases inventory space and costs while decreasing throughput (more glass sizes to shuttle in and out).        c) Certain cutting tables allow the sizes and types of lites from Low Yield Sheets to be manually entered at the cutting table controller with the sizes and types of other of selected lites, then re-optimized to increase yields. Although these features provide flexibility and increase yield, they also cause the cutting table to remain idle during the manual entry process. This greatly reduces production throughput and efficiency.        